项目已跟进到rasa新版本，一些新特性后面尝试后补充。rasa 版本更新太快，本项目滞后最新版本较大，仅供参考，建议根据需要阅读最新rasa文档。
A Chinese task oriented chatbot in IVR(Interactive Voice Response) domain， Implement by rasa nlu and rasa core. This is a demo with toy dataset.
install or update to python 3
pip install rasa_core==0.9.0
this command will install rasa nlu too.
pip install -U scikit-learn sklearn-crfsuite
pip install git+https://github.com/mit-nlp/MITIE.git
_rasa_chatbot/
├── bot.py
├── chat_detection
├── data
│ ├── mobile_nlu_data.json # train data json format
│ ├── mobile_raw_data.txt # train data raw
│ ├── mobile_story.md # toy dialogue train data
│ └── total_word_feature_extractor.dat # pretrained mitie word vector
├── httpserver.py # rasa nlu httpserver
├── __init__.py
├── INSTALL.md
├── ivr_chatbot.yml # rasa nlu config file
├── mobile_domain.yml # rasa core config file
├── projects # pretrained models
│ ├── dialogue
│ └── ivr_nlu
├── README.md
├── tools # tools of data process
└── train.sh # train script of rasa nlu
sh train.sh命令运行耗时较长，模型训练完毕生成：
projects/
└── ivr_nlu
└── demo
├── entity_extractor.dat
├── entity_synonyms.json
├── intent_classifier_sklearn.pkl
├── metadata.json
└── training_data.json
$ python httpserver.py
$ curl -X POST localhost:1235/parse -d '{"q":"我的流量还剩多少"}' | python -m json.tool
{
'q': '我的流量还剩多少',
'intent': 'request_search',
'entities': {
'item': '流量'
}
}
python bot.py train-dialogue模型训练完毕生成：
projects
├── dialogue
│ ├── domain.json
│ ├── domain.yml
│ ├── policy_0_MemoizationPolicy
│ │ ├── featurizer.json
│ │ └── memorized_turns.json
│ ├── policy_1_KerasPolicy
│ │ ├── featurizer.json
│ │ ├── keras_arch.json
│ │ ├── keras_policy.json
│ │ └── keras_weights.h5
│ ├── policy_metadata.json
│ └── stories.md
└── ivr_nlu
python bot.py online_train
Run command below:
python bot.py runExample1:
Bot loaded. Type a message and press enter :
YOU：你是谁
BOT：您好!，我是机器人小热，很高兴为您服务。
YOU：我想看一下消费情况
BOT：您想查询哪个月的消费？
YOU：十月份的
BOT：好，请稍等
BOT：您好，您十月份共消费二十八元。
YOU：好谢谢
BOT：您开心我就开心
BOT：您还想干什么
YOU：没什么了
BOT：Bye， 下次再见
Example2:
Bot loaded. Type a message and press enter :
YOU：给我看看我上月用了多少话费
BOT：好，请稍等
BOT：您好，您上月共消费二十八元。
BOT：您还想干什么
You can train your own MITIE model using following method:
$ git clone https://github.com/mit-nlp/MITIE.git
$ cd MITIE/tools/wordrep
$ mkdir build
$ cd build
$ cmake ..
$ cmake --build . --config Release
$ ./wordrep -e /path/to/your/folder_of_cutted_text_files
/path/to/your/folder_of_cutted_text_files above is a directory path in which has word cutted data files to train. This process may cost one or two days.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent